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In 2012, David Strayer found himself in a research lab, on the outskirts of London, observing something he hadn’t thought possible: extraordinary multitasking. For his entire career, Strayer, a professor of psychology at the University of Utah, had been studying attention—how it works and how it doesn’t. Methods had come and gone, theories had replaced theories, but one constant remained: humans couldn’t multitask. Each time someone tried to focus on more than one thing at a time, performance suffered. Most recently, Strayer had been focussing on people who drive while on the phone. Over the course of a decade, he and his colleagues had demonstrated that drivers using cell phones—even hands-free devices—were at just as high a risk of accidents as intoxicated ones. Reaction time slowed, attention decreased to the point where they’d miss more than half the things they’d otherwise see—a billboard or a child by the road, it mattered not.

The default lingo of high party officials, even on the most solemn occasions, includes banal aphorisms like, “to be turned into iron, the metal must be strong.” Official proclamations and the nightly newscasts speak of “social harmony” and the “Chinese spirit.” In addition to promoting the “China Dream” and a strong work ethic, President Xi Jinping is known for uttering lines like, “Never allow eating the Communist Party’s food and then smashing the Communist Party’s cooking pots.”The government’s propaganda and education machinery moved past the revolutionary bloodthirsty bitterness. Our textbooks are litanies of brutal heroic deeds: “Stop a gun with your chest, hold a bomb in your hands, lie on a fire without moving, until you burn to death.” Nearly every Chinese child still wears a red scarf, “dyed with martyr's blood,” and many grow up singing the young pioneers’ songs: “Always prepared, to perform noble feats, to wipe out our enemy.”

Last month, Brendan Nyhan, a professor of political science at Dartmouth, published the results of a study that he and a team of pediatricians and political scientists had been working on for three years. They had followed a group of almost two thousand parents, all of whom had at least one child under the age of seventeen, to test a simple relationship: Could various pro-vaccination campaigns change parental attitudes toward vaccines? Each household received one of four messages: a leaflet from the Centers for Disease Control and Prevention stating that there had been no evidence linking the measles, mumps, and rubella (M.M.R.) vaccine and autism; a leaflet from the Vaccine Information Statement on the dangers of the diseases that the M.M.R. vaccine prevents; photographs of children who had suffered from the diseases; and a dramatic story from a Centers for Disease Control and Prevention about an infant who almost died of measles. A control group did not receive any information at all. The goal was to test whether facts, science, emotions, or stories could make people change their minds. The result was dramatic: a whole lot of nothing. None of the interventions worked.

The idea that poverty can change the brain has gotten significant attention recently, and not just from those lay readers (a minority, according to recent research) who spend a lot of time thinking about neuroscience. Policy makers and others have begun to apply neuroscientific principles to their thinking about poverty — and some say this could end up harming poor people rather than helping. At The Conversation, the sociologist Susan Sered takes issue with “news reports with headlines like this one: ‘Can Brain Science Help Lift People Out Of Poverty?’” She’s referring to a June story by Rachel Zimmerman at WBUR, about a nonprofit called Crittenton Women’s Union that aims to use neuroscience to help get people out of poverty. Elisabeth Babcock, Crittenton’s chief executive, tells Ms. Zimmerman: “What the new brain science says is that the stresses created by living in poverty often work against us, make it harder for our brains to find the best solutions to our problems. This is a part of the reason why poverty is so ‘sticky.’”

How big do the numbers have to be for insensitivity to begin? Not very, it turns out.

Consider the recent death of the Syrian child Aylan Kurdi when his family braved the choppy seas off the coast of Turkey. The image of Aylan lying face down on the beach captivated the world’s attention and even, in short order, resulted in refugee policy changes in countries as far away as the United States. But 14 Syrian children drowned in the Aegean Sea the next day. Did you notice? Did you care?

To what extent can we and should we aspire to create machines that can outthink us? For example, Netflix has an algorithm that can predict what movies you will like based on the ones you've already seen and rated. Suppose a dating site were to develop a similar algorithm—maybe even a more sophisticated one—and predict with some accuracy which partner would be the best match for you. Whose advice would you trust more? The advice of the smart dating app or the advice of your parents or your friends?

The question, it seems to me, is should we use new genetic technologies only to cure disease and repair injury, or also to make ourselves better-than-well. Should we aspire to become the masters of our natures to protect our children and improve their life prospects?AdvertisementAdvertisement This goes back to the role of accident. Is the unpredictability of the child an important precondition of the unconditional love of parents for children? My worry is that if we go beyond health, we run the risk of turning parenthood into an extension of the consumer society.

So what, exactly, did the algorithm “learn” about the process of dying? And what, in turn, can it teach oncologists? Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?”